تعیین حداقل مجموعه داده جهت ارزیابی کیفیت خاک مطالعه موردی حوضه آبخیز دریاچه چغاخور

Introduction: The mismanagement of natural resources has led to low soil quality and high vulnerability to soil erosion in most parts of Iran. To have a sustainable soil quality, the assessment of effective soil quality indicators are required. The soil quality is defined as the capacity of a soil to function within natural and/or managed ecosystem boundaries. Among approaches which are suggested for soil quality assessment like soil card design, test kits, geostatistical methods and soil quality indices (SQIs), SQIs are formed by combination of soil indicators which resulted from integration evaluation of soil physical, chemical and/or biological properties and processes complement by existing/measureable data, sensitive to land use changes, management practices and human activities and could be applied in different ecosystems. As the measurement and monitoring of all soil quality indicators is laborious and costly, many researchers focused on limited soil quality indicators. There are many methods for identification and determination of minimum data set that influence on soil quality such as linear and multiple regression analysis, pedotransfer functions, scoring functions, principle component analysis and discriminant analysis. Among these methods, principle component analysis is commonly used because it is able to group related soil properties into small set of independent factors and to reduce redundant information in original data set. The objective of this research was to investigate the effects of land use change on soil quality indicators and also the determination of minimum effective soil quality indicators for assessment of soil quality in Choghakhor Lake basin, Chaharmahal and Bakhtiari province, Iran.
Materials and Methods: To meet the goal, Latin hypercube sampling method was applied by using slope, land use and geological maps and 125 composite soil samples were collected from soil surface (0-20 cm). After pretreatments, 27 physical and chemical soil properties like clay, sand and silt content, bulk density (ρb), porosity, organic carbon (OC), particulate organic carbon in macro aggregate (POCmac), particulate organic carbon in micro aggregates (POCmic), proportion of particulate organic carbon in macro aggregates to micro aggregates (POCmac/mic), mean weight diameter (MWD), macro porosity (Mac pore), air content, available water content (AWC), relative water content (RWC), effective porosity (Feff), Dexter index (S), porosity, acidity (pH), electrical conductivity (EC), Nitrogen (N), Phosphorous (P), Iron (Fe), manganese (Mn), Zinc (Zn), Cadmium (Cd), lead (Pb), Copper (Cu) and Cobalt (Co) were measured using appropriate methods.
Results and Discussion: The impact of different land use types on soil quality was evaluated by measuring several soil properties that are sensitive to stress or disturbance and comparison of them. The results showed that measured values of OC, POCmac, POCmic, POCmac/mic, P, Fe, Zn, Mn, Cu, ρb, MWD, AWC, air content and S were in order orchards > crop land > good rangelands > dry lands > weak rangelands. In this region, land use changes have different effects on soil quality. The alternation of good pasture lands to orchard and crop lands caused to enhancement of soil quality parameters. The variation of good pasture to dry land and degradation of good pasture in this area led to decreasing of soil quality. The principle component analysis (PCA) was employed as a data reduction tool to select the most appropriate indicators of site potential for the study area from the list of indicators. Based on PCA, 8 components with eigenvalues ≥ 1 were selected that explained 99.96 percent of variance. The prominent eigenvectors in components were selected using Selection Criterion (SC). The results revealed that the most important component, was the first component with the most dominant measured soil property of Cu. 12 soil quality parameters base on SC were determined in the first component. Stepwise discriminate analysis also was applied for determination significant soil quality indicators from 12 soil parameters. Our result showed that the minimum data set influencing on soil quality were Zn followed by POCmac/mic, clay %, Cu, Mn and P, respectively.
Conclusion: The results suggested that the permanent crop management (Orchard and crop land) had generally a positive impact on soil quality, while dry land and degradation of good pasture had a negative impact on soil quality. Our study suggested that the PCA method and stepwise discriminant analysis for determination of minimum data set can be used in Chughakhur lake basin. In this study from27 of physical and chemical soil properties, the fertility factors such as the content of Zn, Cu, Mn and P and the proportion of particle organic carbon in macro aggregate to micro aggregate and also soil texture components can be used to the minimum data set that evaluates soil quality. These parameters mostly depend on soil management system.